NIHR awards £12 million to artificial intelligence research to help understand multiple long-term conditions
The NIHR has awarded almost £12 million to new research that will use advanced data science and artificial intelligence (AI) methods to identify and understand clusters of multiple long-term conditions and develop ways to prevent and treat them.
An estimated 14 million people in England are living with two or more long-term conditions, with two-thirds of adults aged over 65 expected to be living with multiple long-term conditions by 2035.
People who develop multiple long-term conditions often do not have a random assortment of diseases but rather a largely predictable cluster of conditions. Developing a better understanding of these disease clusters, including how they develop over the course of a person’s life and are influenced by wider determinants of health, requires novel research and analytical tools that can operate across complex datasets.
The Artificial Intelligence for Multiple Long-Term Conditions (AIM) call, in partnership with NHSX NHS AI Lab, funds research that combines data science and AI methods with health, care and social science expertise to identify new clusters of disease and understand how multiple long-term conditions develop over the life course.
The call will fund up to £23 million of research in two waves, supporting a pipeline of research and capacity building in multiple long-term conditions research. The first wave has invested nearly £12 million into three Research Collaborations, nine Development Awards and a Research Support Facility.
The three Research Collaborations funded by this first wave of the AIM call are partnerships between leading academic institutions that bring together a broad range of expertise from health and care, AI and social science.
The Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC) project will identify the most common clusters of long-term conditions and examine whether people inherit a tendency to get particular combinations of conditions from their parents. It will also consider how socioeconomic and geographical factors contribute to multiple long-term conditions.
The collaboration, led by Professor Bruce Guthrie at the University of Edinburgh, will develop new methods to help GPs or hospital doctors predict when a patient might be at high risk of adverse outcomes because they have other conditions, and develop new models of care to improve the outcomes of those at highest risk.
The OPTIMising therapies, discovering therapeutic targets and AI-assisted clinical management for patients Living with complex multimorbidity (OPTIMAL) study, led by Dr Thomas Jackson and Dr Krishnarajah Nirantharakumar at the University of Birmingham, aims to understand how different combinations of long-term conditions and the medicines taken for these diseases interact over time to worsen or improve a patient’s health. With the use of AI, this study will produce computer programmes and tools that will help doctors improve the choice of drugs in these patients.
The Development and Validation of Population Clusters for Integrating Health and Social Care study, led by Dr Hajira Dambha-Miller at University of Southampton and Professor Andrew Farmer at University of Oxford, will use machine learning to identify clusters of diseases on the basis of social factors, such as mobility and finances, as well as health and biological markers. This information will be used to understand the health and social care needs of people within each cluster over time and develop tailored treatment approaches that join up health and social care.
Building capacity and capability
Through this funding call, NIHR is also growing sustainable capacity and capability for multidisciplinary research in multiple long-term conditions.
A new Research Support Facility (RSF), based at The Alan Turing Institute, will offer AI and advanced data science support to the research teams funded by AIM and foster collaboration. The facility, led by Dr Kirstie Whitaker and Professor Chris Holmes, will support data access and analysis and the use of AI to minimise administrative costs of research, speeding up research and its uptake and impact.
This first wave of the AIM call has also funded nine Development Awards, to support researchers to develop new collaborations and undertake proof of concept work. These Development Award holders then apply for a Research Collaboration award in wave 2 of the call, creating a pipeline of data science and AI translational research projects and growing capacity and capability for multidisciplinary research in multiple long-term conditions.
NIHR’s commitment to tackling multiple long-term conditions
Improving the lives of people with multiple long-term conditions and their carers through research is an area of strategic focus for the NIHR, with our ambitions set out in our NIHR Strategic Framework for Multiple Long-Term Conditions Research.
Professor Lucy Chappell, NIHR Chief Executive and chair of the AIM funding committee, said: “This large-scale investment in research will improve our understanding of clusters of multiple long-term conditions, including how they develop over a person’s lifetime.
“Over time, findings from this new research will point to solutions that might prevent or slow down the development of further conditions over time. We will also look at how we shape treatment and care to meet the needs of people with multiple long-term conditions and carers.”
To date, NIHR has invested £11million into research on multiple long-term conditions through two calls in partnership with the Medical Research Council, offering both pump-priming funds and funding to tackle multimorbidity at scale.
- OPTIMising therapies, discovering therapeutic targets and AI assisted clinical management for patients Living with complex multimorbidity (OPTIMAL study) - University of Birmingham, The University of Manchester, University Hospitals Birmingham NHS Foundation Trust, NHS Greater Glasgow & Clyde, University of St Andrews, Medicines and Healthcare products Regulatory Agency
- The development and validation of population clusters for integrating health and social care: A mixed-methods study on Multiple Long-Term Conditions - University of Southampton, University of Oxford, University of Kent, University of Nottingham, University of Leicester
- Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC) - University of Edinburgh, The Roslin Institute, NHS Lothian, University College London
Research Support Facility
- The Alan Turing Institute, Swansea University, University of Edinburgh, MRC Harwell
- Artificial intelligence for identifying new disease clusters in patients with immune-mediated inflammatory conditions: A Proof-of-Concept Study - University of Bath
- Inflammatory drivers of long-term comorbidity trajectories: an AI investigation of multimorbidity (inflAIM) - University of East Anglia
- DECODE: mapping the challenges and requirements for Data-driven, machinE learning aided stratification and management of multiple long-term COnditions in adults with intellectual DisabilitiEs - Leicestershire Partnership NHS Trust
- Developing a Multidisciplinary Ecosystem to study Lifecourse Determinants of Complex Mid-life Multimorbidity using Artificial Intelligence (MELD) - University of Southampton
- CoMPuTE: Complex Multimorbidity Phenotypes, Trends, and Endpoints - University of Oxford
- Characterising the dynamic inter-relationships between polypharmacy and multiple long-term conditions. Using artificial intelligence (AI) to map patient journeys into multimorbidity clusters across the UK - University of Newcastle upon Tyne
- Using Artificial Intelligence to Tackle Multiple Long-Term Conditions - Multi-morbidity in South Yorkshire - University of Sheffield
- Northern Ireland Multimorbidity Research and Discovery (NIMRAD) Consortium - University of Ulster
- Using deep learning approaches to examine serious mental illness and physical multimorbidity across the life-course: from mechanisms towards novel interventions - Queen Mary University of London